metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - generated
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: generated
          type: generated
          config: sroie
          split: test
          args: sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9979716024340771
          - name: Recall
            type: recall
            value: 0.9979716024340771
          - name: F1
            type: f1
            value: 0.9979716024340771
          - name: Accuracy
            type: accuracy
            value: 0.9997893406361913

layoutlmv3-finetuned-invoice

This model is a fine-tuned version of microsoft/layoutlmv3-base on the generated dataset. It achieves the following results on the evaluation set:

Loss: 0.0019
Precision: 0.9980
Recall: 0.9980
F1: 0.9980
Accuracy: 0.9998

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.1069	0.946	0.9594	0.9527	0.9943
No log	4.0	200	0.0229	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0158	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0113	0.972	0.9858	0.9789	0.9971
0.1416	10.0	500	0.0103	0.9800	0.9919	0.9859	0.9979
0.1416	12.0	600	0.0047	0.9980	0.9959	0.9970	0.9996
0.1416	14.0	700	0.0035	0.9939	0.9959	0.9949	0.9994
0.1416	16.0	800	0.0044	0.9980	0.9959	0.9970	0.9996
0.1416	18.0	900	0.0027	0.9980	0.9959	0.9970	0.9996
0.0049	20.0	1000	0.0019	0.9980	0.9980	0.9980	0.9998
0.0049	22.0	1100	0.0017	1.0	1.0	1.0	1.0
0.0049	24.0	1200	0.0041	0.9960	0.9980	0.9970	0.9996
0.0049	26.0	1300	0.0033	0.9960	0.9980	0.9970	0.9996
0.0049	28.0	1400	0.0029	0.9960	0.9980	0.9970	0.9996
0.0029	30.0	1500	0.0018	0.9960	0.9980	0.9970	0.9996
0.0029	32.0	1600	0.0019	0.9960	0.9980	0.9970	0.9996
0.0029	34.0	1700	0.0016	0.9980	0.9980	0.9980	0.9998
0.0029	36.0	1800	0.0017	0.9980	0.9980	0.9980	0.9998
0.0029	38.0	1900	0.0018	0.9980	0.9980	0.9980	0.9998
0.0019	40.0	2000	0.0014	0.9980	0.9980	0.9980	0.9998

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1